Evidence map›Paper›PMID 40124408›Full record

SynthesisFrontiers in public health2025

Move your body, stay away from depression: a systematic review and meta-analysis of exercise-based prevention of depression in middle-aged and older adults.

Xiao-Dong Zhao, Sae-Sook Oh, Zheng Zhang, Chong Wang

Abstract readSystematic ReviewMeta-Analysis
In one paragraph

Synthesis in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Xiao-Dong ZhaoCollege of Sports Science, Kyonggi University, Suwon, Gyeonggi, Republic of Korea.
Sae-Sook OhCollege of Sports Science, Kyonggi University, Suwon, Gyeonggi, Republic of Korea.
Zheng ZhangCollege of Sports Science, Kyonggi University, Suwon, Gyeonggi, Republic of Korea.
Chong WangCollege of Sports Science, Northwest Normal University, Lanzhou, Gansu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aimed to systematically assess the efficacy of exercise interventions in preventing depression among middle-aged and older adults. It further sought to dissect the impact of various intervention factors on the exercise-depression nexus within this demographic. Methods: We conducted a comprehensive search across multiple databases including PubMed, Embase, Web of Science, and the Cochrane Library, spanning from their inception through April 2024. The initial search yielded several studies, from which 11 papers involving a total of 792 subjects were selected based on predetermined inclusion criteria. Results: Our meta-analysis revealed a significant overall effect size (SMD = -3.64) with a 95% confidence interval of [-4.81, -2.48] and a Conclusion: Exercise interventions, particularly balance and gentle training as well as functional training, significantly reduce the risk of depression among middle-aged and older adults. The most pronounced effects were observed in group exercise settings and with exercise sessions lasting 30-40 min. Interventions of medium duration generally outperformed shorter-term interventions. Despite these promising results, the observed heterogeneity and other potential study limitations suggest a need for further research involving larger samples and more comprehensive study designs to refine and optimize exercise protocols for depression prevention in this population.

Indexed as

DepressionExerciseExercise TherapyAgedFemaleHumansMaleMiddle Ageddepressionexercisemeta-analysisolder adultprevent

Identifiers

PMID40124408
PMCPMC11925714

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.